Elad Hoffer

1.8k citations
11 papers · 282 · h-index 5

Impact in

Papers in

Journals
Lecture notes in computer science (1 paper)Repository for Publications and Research Data (ETH Zurich) (1 paper)arXiv (Cornell University) (6 papers)Neural Information Processing Systems (2 papers)

In The Last Decade

Elad Hoffer

11 papers receiving 271 citations

Peers

Elad Hoffer
Comparison fields: 5 of 52
  • Computational Mathematics 7
  • Computer Vision and Pattern Recognition 198
  • Artificial Intelligence 170
  • Media Technology 15
  • Hardware and Architecture 7
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Ziran Wei China
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Yury Nahshan Israel
Michael Figurnov Russia
Ruizhou Ding United States
Yanxi Li China
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Citations per field
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Citations per year

Countries citing papers authored by Elad Hoffer

Since Specialization
Citations

This map shows the geographic impact of Elad Hoffer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Elad Hoffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elad Hoffer more than expected).

Fields of papers citing papers by Elad Hoffer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elad Hoffer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Elad Hoffer. The network helps show where Elad Hoffer may publish in the future.

Co-authors

The 9 scholars most cited alongside Elad Hoffer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Elad Hoffer Line = papers co-authored together Elad Hoffer links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2020103
2
Scalable methods for 8-bit training of neural networks
201856
3
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
201752
4
ACIQ: Analytical Clipping for Integer Quantization of neural networks
201842
5
Norm matters: efficient and accurate normalization schemes in deep networks
201817
6
Semi-supervised deep learning by metric embedding
20174
7
Quantized Back-Propagation: Training Binarized Neural Networks with Quantized Gradients
20184
8 20231
9
Neural gradients are lognormally distributed: understanding sparse and quantized training.
20201
10
Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning.
20181
11
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
20201

About Elad Hoffer

Elad Hoffer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Electrical and Electronic Engineering and Computational Mechanics, having authored 11 papers that have together received 282 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (6 papers), Advanced Neural Network Applications (5 papers), Neural Networks and Applications (3 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Memory and Neural Computing (2 papers), Model Reduction and Neural Networks (2 papers), Numerical Methods and Algorithms (1 paper) and Ferroelectric and Negative Capacitance Devices (1 paper). The work is most often cited by research in Computational Mathematics (7 citations), Computer Vision and Pattern Recognition (198 citations), Artificial Intelligence (170 citations), Media Technology (15 citations) and Hardware and Architecture (7 citations). Elad Hoffer has collaborated with scholars based in Israel, United States and Switzerland. Frequent co-authors include Daniel Soudry, Itay Hubara, Ron Banner, Tal Ben‐Nun, Torsten Hoefler, Niv Giladi, Yury Nahshan, Nir Ailon and Tomer Michaeli. Their work appears in journals such as Lecture notes in computer science, Repository for Publications and Research Data (ETH Zurich), arXiv (Cornell University) and Neural Information Processing Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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